In this paper, we present a notion of differential privacy (DP) for data that comes from different classes.
Stakeholders in electricity delivery infrastructure are amassing data about their system demand, use, and operations.
It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers.
We present an algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature.
Ranked #10 on Image Super-Resolution on FFHQ 256 x 256 - 4x upscaling (PSNR metric)
This work identifies and addresses two important technical challenges in single-image super-resolution: (1) how to upsample an image without magnifying noise and (2) how to preserve large scale structure when upsampling.